Tag: logs

Posted in software

Coralogix raises $25 million to parse software logs with AI

Coralogix, which analyzes software logs with AI, today announced $25 million in new funding and launched a real-time analytics solution that allows customers to pay according to data priority instead of volume. This allows them to get queries, alerts, and machine learning capabilities without using storage.

About 50% of logging statements don’t include any information about critical things like variable state at the time of an error, according to GitHub and OverOps surveys. That may be why developers spend an estimated half of their time on troubleshooting and bug-fixing.

San Francisco-based Coralogix, which was founded in 2014, provides AI analytics solutions for a host of software development challenges. Its suite automatically clusters log records back to their patterns and identifies connections among those patterns, forming baseline flows for comparison and future study. Scaling from hundreds to millions of logs — with integrations for popular languages and platforms like Docker, Python, Heroku, .NET, Kubernetes, and Java — Coralogix spotlights anomalies and affords developers access to a suite of identification, visualization, and remediation tools.

Coralogix is hosted as an Amazon Web Services app, and it hooks into Jenkins and other popular continuous integration/continuous delivery tools to ingest updates pushed to production systems. A query in Coralogix pulls up grouped results — highlighting when and where something occurred, any associated parameters, and the total percentage of those occurrences within the logs.


It also creates “component-level” insights from log data, in part by applying machine learning to software releases to spot quality issues. The service can enrich weblogs with IP blacklists to identify suspicious activity while issuing alerts when errors or critical log entries occur. In addition, Coralogix has an integrated security information and event management and intrusion detection system that taps machine learning to pinpoint anomalies within network packets, server events, and audit